Higher Education Research Experiences at ORNL

Become part of HERE at ORNL

  • Experience the thrill of research or technical projects at a cutting edge national laboratory and camaraderie with prestigious scientists, researchers, and engineers
  • Meet and collaborate with the people who are world and international experts in fields that interest you
  • Contribute to the U.S. technical prowess that will enhance living standards and set the nation at the top of a global community
  • Work on solutions to pressing scientific problems

The HERE at ORNL program includes research participation programs for both faculty and students at various academic levels. To learn more, select the academic level that most closely corresponds to your highest level achieved.


More information–http://www.orau.org/ornl/hereatornl/

CRA Bulletin New Undergraduate Research Opportunities Listing Service

The Computing Research Association’s Education Committee (CRA-E) is pleased to provide a new “undergraduate research listing service” for faculty and other researchers to advertise (at no cost) undergraduate research opportunities and for undergraduates to find such opportunities.  The site can be found here:http://conquer.cra.org/research-opportunities.

This site can be used to advertise individual summer positions, research programs, and any other opportunities for undergraduates to engage in research in the computing field. If you have a research opportunity available, please post it here: http://conquer.cra.org/post-a-research-opportunity.

This service is a new feature on CRA-E’s Conquer site which provides resources for students interested in computing research and graduate school and for faculty advisers and mentors.

Please share the listing site and Conquer with your colleagues and students. We encourage you to link to it from your department site.

Rosen awarded ALMA Development Project grant from the National Radio Astronomy Observatory

Paul Rosen along with Bei Wang (University of Utah), Chris Johnson (University of Utah), Jeff Kern (NRAO), and Betsy Mills (NRAO) received a 1-year ALMA Development Project grant from the National Radio Astronomy Observatory for $185k. The grant is titled “Feature Extraction and Visualization of AMLA Data Cubes through Topological Data Analysis”.

The project is a feasibility study for applying forms of data analysis and visualization never before tested by the ALMA community. Through contour tree-based Topological Data Analysis, we seek to improve upon existing data cube analysis and visualization. This will come in the form of improved accuracy and speed in finding features, which are robust to noise, and a better visual description of features once identified. We will build prototype software, which creates visualizations that help in characterizing and analyzing the spectra of complex spectral line sources within a given data cube.

Link to the original article

USF/COE PhD Dissertation Committee Requirements

Our College requires that each PhD Dissertation Committee have:
   (a) 3 faculty from the department (Major Professor and two other faculty)
   (b) 1 member outside the department but within the College
   (c) 1 member outside the College (could be outside USF)
Therefore, a committee will have at least 5 members.

The College also recommends the Dissertation Defense Chair to be a non-committee member and from outside the department. Our department strictly follows this rule just to avoid any CoI.

Rosen Receives NSF Award

Paul Rosen along with Bei Wang (University of Utah) received a NSF grant with additional collaborative award for Carlos Scheidegger (University of Arizona) for 4 years totaling $1.03M. The grant is titled “III: Medium: Collaborative Research: Topological Data Analysis for Large Network Visualization.” Rosen’s portion of this grant, to be subcontracted from the University of Utah, is $325K.

This project leverages topological methods to develop a new class of data analysis and visualization techniques to understand the structure of networks. Networks are often used in modeling social, biological and technological systems, and capturing relationships among individuals, businesses, and genomic entities. Understanding such large, complex data sources is highly relevant and important in application areas including brain connectomics, epidemiology, law enforcement, public policy and marketing. The proposed research will be evaluated over multiple data sources, including but not limited to large social, communication and brain network datasets. Furthermore, the new approaches developed in this project will be integrated into growing data analysis curricula, shared through developing workshops, and used as topics to continue attracting underrepresented groups into STEM fields and computer science specifically.

For more information about the award, click here and for more information about the award with amendments, click here.

Link to the original article

New Accounts

The account created for you will work on both the file server and the compute/terminal servers. The usernames, passwords, home directories, and project folders are synchronized between both. 


saav.cspaul.com — file server
hermes.cspaul.com — compute/terminal
zoidberg.cspaul.com — 

Change your password

    See instructions below for changing your password on the compute server; OR

Go to https://saav.cspaul.com:5001/webman/3rdparty/DirectoryServer/profile.cgi

Accessing the compute servers

I have multiple machines, but sometimes they go down. Try all, and if none work, contact me.

    ssh username@hermes.cspaul.com
    ssh username@zoidberg.cspaul.com

Change your password:
    $ passwd

Your home directory should be mounted from the file server:
    $ cd ~

Access the project files:
    $ cd /saav/projects/

Access the data files:
    $ cd /saav/data/

Accessing the file server

Access to remote file services require that you be on the USF network. You can do this by working from anywhere on campus or using the USF VPN. If for some reason you can’t use USF VPN, please contact Paul.

VPN into USF:

Connect to file server with Mac:
    Finder -> Go -> Connect to Server
    Server: afp://saav.cspaul.com
    Username: username@saav.cspaul.com

Connect to file server with Windows:
    Server: \\saav.cspaul.com
    Username: username@saav.cspaul.com

Navigate to:
\projects, \home, or \data

Cloud Station Drive

Basically this works like google drive or dropbox, except the data is stored on the file server.

Windows Download

Mac Download


Ten challenges in 3D printing

Three dimensional printing has gained considerable interest lately due to the proliferation of inexpensive devices as well as open source software that drive those devices. Public interest is often followed by media coverage that tends to sensationalize technology. Based on popular articles, the public may create the impression that 3D printing is the Holy Grail; we are going to print everything as one piece, traditional manufacturing is at the brink of collapse, and exotic applications, such as cloning a human body by 3D bio-printing, are just around the corner. The purpose of this paper is to paint a more realistic picture by identifying ten challenges that clearly illustrate the limitations of this technology, which makes it just as vulnerable as anything else that had been touted before as the next game changer.

Ten challenges in 3D printing
W Oropallo, LA Piegl
Engineering with Computers 32 (1), 135-148

Robustness-based simplification of 2d steady and unsteady vector fields

Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness which enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory and has minimal boundary restrictions. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets. We show local and complete hierarchical simplifications for steady as well as unsteady vector fields.

Robustness-based simplification of 2d steady and unsteady vector fields
P Skraba, B Wang, G Chen, P Rosen
IEEE transactions on visualization and computer graphics 21 (8), 930-944